Active Monitoring: How Agentic AI Auto-Heals and Protects Enterprise Data Pipelines

This article discusses the limitations of static alert thresholds in enterprise data pipelines, such as false positives and alert fatigue, and introduces agentic AI monitoring systems that autonomously reason about deviations, correlate root causes, and perform automated rollback and recovery. It covers the architecture including metric collection, deviation detection, anomalous trace analysis, and integration with Dremio, while also addressing limits of autonomous recovery and building a monitoring knowledge base. Aimed at IT and data engineering professionals, it provides a technical overview of next-generation pipeline reliability.